Microbiome_analysis_16s_sequencing
Mandira Katuwal Bhattarai
15/01/2022
R Markdown
R-script for microbiome analysis following this tutorial: https://murraycadzow.github.io/2021-obss-day4/06-importing-into-R/index.html
# load the packages
library('phyloseq')
library('tibble')
library('ggplot2')
library('ape')
library('vegan')## Loading required package: permute## Loading required package: lattice## This is vegan 2.5-7# set the working directory
setwd('/Users/mandy/Desktop/16s-data-analysis/16s_R_pipeline' )Importing raw data
# Import frequency table
import_table <- read.table('raw_data/otu_frequency_table.tsv',header=TRUE,sep='\t',row.names=1, comment.char = "")
head(import_table)## A1 A10 A11 A12 A13 A15 A16 A17 A18 A19 A2 A20 A21
## 0tu.1 113349 78465 68476 18916 58723 42467 177164 16 16 1 57565 447 690
## 0tu.10 706 146 166 2 5135 4 7862 42 56 9 33 49 23
## 0tu.100 175 2 0 0 0 0 989 1 2 1 1 0 4
## 0tu.1002 23 0 0 0 0 0 0 0 0 0 1 0 0
## 0tu.1007 0 0 0 0 0 0 24 0 0 0 1 0 0
## 0tu.101 0 0 0 0 0 0 0 0 0 1 0 2 2
## A22 A23 A24 A25 A26 A27 A28 A29 A3 A30 A31 A32 A33
## 0tu.1 153 247 102 247 782 224 4851 99 71375 58889 44823 49375 25282
## 0tu.10 54 9 26 13 1916 4303 12191 20 185 8566 178 6508 290
## 0tu.100 2 1 1 1 348 155 593 0 6 8 11 163 26
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 1 0 0
## 0tu.1007 0 0 0 0 0 1 6 0 0 0 0 7 0
## 0tu.101 0 0 1 4 0 32 0 0 1 0 0 0 1
## A34 A35 A4 A5 A6 A7 A8 A9 C1 C10 C11 C12 C13 C16
## 0tu.1 7776 184008 72355 618 56973 32874 34090 1433 3 3 1 0 5 6
## 0tu.10 15260 732 22 4119 48 376 7578 4754 22 184 67 439 634 0
## 0tu.100 717 20 0 1126 0 8 814 434 0 0 0 0 0 0
## 0tu.1002 0 0 13 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 3 2 0 13 0 0 1 2 0 0 0 0 0 0
## 0tu.101 0 0 5 0 0 0 0 25 0 0 0 0 1 0
## C17 C18 C19 C2 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C3 C30 C31
## 0tu.1 9 7 2 0 4 2 0 0 0 9 10 4 7 1 4 7 1
## 0tu.10 197 776 35 97 59 8 106 170 111 58 117 1295 1556 260 139 448 252
## 0tu.100 0 0 0 1 0 0 0 0 0 0 5 0 1 0 0 0 0
## 0tu.1002 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 0 0 0 0 0 0 1 1 1 0 262 431 275 0 245 725
## C32 C33 C34 C35 C36 C37 C38 C39 C4 C40 C5 C6 C7 C8 C9 L1 L10
## 0tu.1 1 62 0 1 2 5 3 2 5 0 7 2 0 8 3 1 4
## 0tu.10 942 77 290 948 465 1498 401 316 233 516 7 8 105 68 223 432 26
## 0tu.100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 54 264 385 1153 196 464 861 590 0 468 0 1 0 0 0 0 0
## L11 L12 L13 L14 L15 L16 L17 L18 L19 L2 L20 L21 L22 L23 L24
## 0tu.1 4 11 600 7 38 34 5709 4 143 123 242 190 170 84 40
## 0tu.10 1707 293 6 529 12 4 4272 4854 11342 2819 1265 2767 385 318 2221
## 0tu.100 0 0 0 2 0 0 196 3 0 0 4 1 4 0 2
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0
## L3 L4 L5 L6 L7 L8 L9 N1 N2 P1
## 0tu.1 2 14 14 227 7 5 5 382 4 88
## 0tu.10 2559 3423 197 1193 2133 29 56 17 3 9
## 0tu.100 0 0 3 0 1 0 0 0 0 0
## 0tu.1002 0 0 0 0 0 0 0 0 0 1
## 0tu.1007 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 0 0 0 0 0 0 0 0 0Now convert to a matrix for Phyloseq
otumat <- as.matrix(import_table)
head(otumat)## A1 A10 A11 A12 A13 A15 A16 A17 A18 A19 A2 A20 A21
## 0tu.1 113349 78465 68476 18916 58723 42467 177164 16 16 1 57565 447 690
## 0tu.10 706 146 166 2 5135 4 7862 42 56 9 33 49 23
## 0tu.100 175 2 0 0 0 0 989 1 2 1 1 0 4
## 0tu.1002 23 0 0 0 0 0 0 0 0 0 1 0 0
## 0tu.1007 0 0 0 0 0 0 24 0 0 0 1 0 0
## 0tu.101 0 0 0 0 0 0 0 0 0 1 0 2 2
## A22 A23 A24 A25 A26 A27 A28 A29 A3 A30 A31 A32 A33
## 0tu.1 153 247 102 247 782 224 4851 99 71375 58889 44823 49375 25282
## 0tu.10 54 9 26 13 1916 4303 12191 20 185 8566 178 6508 290
## 0tu.100 2 1 1 1 348 155 593 0 6 8 11 163 26
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 1 0 0
## 0tu.1007 0 0 0 0 0 1 6 0 0 0 0 7 0
## 0tu.101 0 0 1 4 0 32 0 0 1 0 0 0 1
## A34 A35 A4 A5 A6 A7 A8 A9 C1 C10 C11 C12 C13 C16
## 0tu.1 7776 184008 72355 618 56973 32874 34090 1433 3 3 1 0 5 6
## 0tu.10 15260 732 22 4119 48 376 7578 4754 22 184 67 439 634 0
## 0tu.100 717 20 0 1126 0 8 814 434 0 0 0 0 0 0
## 0tu.1002 0 0 13 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 3 2 0 13 0 0 1 2 0 0 0 0 0 0
## 0tu.101 0 0 5 0 0 0 0 25 0 0 0 0 1 0
## C17 C18 C19 C2 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C3 C30 C31
## 0tu.1 9 7 2 0 4 2 0 0 0 9 10 4 7 1 4 7 1
## 0tu.10 197 776 35 97 59 8 106 170 111 58 117 1295 1556 260 139 448 252
## 0tu.100 0 0 0 1 0 0 0 0 0 0 5 0 1 0 0 0 0
## 0tu.1002 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 0 0 0 0 0 0 1 1 1 0 262 431 275 0 245 725
## C32 C33 C34 C35 C36 C37 C38 C39 C4 C40 C5 C6 C7 C8 C9 L1 L10
## 0tu.1 1 62 0 1 2 5 3 2 5 0 7 2 0 8 3 1 4
## 0tu.10 942 77 290 948 465 1498 401 316 233 516 7 8 105 68 223 432 26
## 0tu.100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 54 264 385 1153 196 464 861 590 0 468 0 1 0 0 0 0 0
## L11 L12 L13 L14 L15 L16 L17 L18 L19 L2 L20 L21 L22 L23 L24
## 0tu.1 4 11 600 7 38 34 5709 4 143 123 242 190 170 84 40
## 0tu.10 1707 293 6 529 12 4 4272 4854 11342 2819 1265 2767 385 318 2221
## 0tu.100 0 0 0 2 0 0 196 3 0 0 4 1 4 0 2
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0
## L3 L4 L5 L6 L7 L8 L9 N1 N2 P1
## 0tu.1 2 14 14 227 7 5 5 382 4 88
## 0tu.10 2559 3423 197 1193 2133 29 56 17 3 9
## 0tu.100 0 0 3 0 1 0 0 0 0 0
## 0tu.1002 0 0 0 0 0 0 0 0 0 1
## 0tu.1007 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 0 0 0 0 0 0 0 0 0Now create an OTU object using the function otu_table
OTU_all <- otu_table(otumat, taxa_are_rows = TRUE)
head(OTU_all)## OTU Table: [6 taxa and 99 samples]
## taxa are rows
## A1 A10 A11 A12 A13 A15 A16 A17 A18 A19 A2 A20 A21
## 0tu.1 113349 78465 68476 18916 58723 42467 177164 16 16 1 57565 447 690
## 0tu.10 706 146 166 2 5135 4 7862 42 56 9 33 49 23
## 0tu.100 175 2 0 0 0 0 989 1 2 1 1 0 4
## 0tu.1002 23 0 0 0 0 0 0 0 0 0 1 0 0
## 0tu.1007 0 0 0 0 0 0 24 0 0 0 1 0 0
## 0tu.101 0 0 0 0 0 0 0 0 0 1 0 2 2
## A22 A23 A24 A25 A26 A27 A28 A29 A3 A30 A31 A32 A33
## 0tu.1 153 247 102 247 782 224 4851 99 71375 58889 44823 49375 25282
## 0tu.10 54 9 26 13 1916 4303 12191 20 185 8566 178 6508 290
## 0tu.100 2 1 1 1 348 155 593 0 6 8 11 163 26
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 1 0 0
## 0tu.1007 0 0 0 0 0 1 6 0 0 0 0 7 0
## 0tu.101 0 0 1 4 0 32 0 0 1 0 0 0 1
## A34 A35 A4 A5 A6 A7 A8 A9 C1 C10 C11 C12 C13 C16
## 0tu.1 7776 184008 72355 618 56973 32874 34090 1433 3 3 1 0 5 6
## 0tu.10 15260 732 22 4119 48 376 7578 4754 22 184 67 439 634 0
## 0tu.100 717 20 0 1126 0 8 814 434 0 0 0 0 0 0
## 0tu.1002 0 0 13 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 3 2 0 13 0 0 1 2 0 0 0 0 0 0
## 0tu.101 0 0 5 0 0 0 0 25 0 0 0 0 1 0
## C17 C18 C19 C2 C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C3 C30 C31
## 0tu.1 9 7 2 0 4 2 0 0 0 9 10 4 7 1 4 7 1
## 0tu.10 197 776 35 97 59 8 106 170 111 58 117 1295 1556 260 139 448 252
## 0tu.100 0 0 0 1 0 0 0 0 0 0 5 0 1 0 0 0 0
## 0tu.1002 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 0 0 0 0 0 0 1 1 1 0 262 431 275 0 245 725
## C32 C33 C34 C35 C36 C37 C38 C39 C4 C40 C5 C6 C7 C8 C9 L1 L10
## 0tu.1 1 62 0 1 2 5 3 2 5 0 7 2 0 8 3 1 4
## 0tu.10 942 77 290 948 465 1498 401 316 233 516 7 8 105 68 223 432 26
## 0tu.100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 54 264 385 1153 196 464 861 590 0 468 0 1 0 0 0 0 0
## L11 L12 L13 L14 L15 L16 L17 L18 L19 L2 L20 L21 L22 L23 L24
## 0tu.1 4 11 600 7 38 34 5709 4 143 123 242 190 170 84 40
## 0tu.10 1707 293 6 529 12 4 4272 4854 11342 2819 1265 2767 385 318 2221
## 0tu.100 0 0 0 2 0 0 196 3 0 0 4 1 4 0 2
## 0tu.1002 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 0tu.1007 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 7 0 0 0 0 0 0 0 0 0 0 0 0 0
## L3 L4 L5 L6 L7 L8 L9 N1 N2 P1
## 0tu.1 2 14 14 227 7 5 5 382 4 88
## 0tu.10 2559 3423 197 1193 2133 29 56 17 3 9
## 0tu.100 0 0 3 0 1 0 0 0 0 0
## 0tu.1002 0 0 0 0 0 0 0 0 0 1
## 0tu.1007 0 0 0 0 0 0 0 0 0 0
## 0tu.101 0 0 0 0 0 0 0 0 0 0The total number of sequences for each sample
colSums(OTU_all)## A1 A10 A11 A12 A13 A15 A16 A17 A18 A19 A2
## 180344 83627 69685 65585 78971 48091 553080 649 1189 425 58335
## A20 A21 A22 A23 A24 A25 A26 A27 A28 A29 A3
## 1351 1877 692 1008 890 980 59016 64992 220645 832 77216
## A30 A31 A32 A33 A34 A35 A4 A5 A6 A7 A8
## 142596 54753 104439 67731 325583 331039 104809 219435 58010 59057 205737
## A9 C1 C10 C11 C12 C13 C16 C17 C18 C19 C2
## 302729 36845 55258 74592 68346 153155 264 54963 75035 59585 46334
## C20 C21 C22 C23 C24 C25 C26 C27 C28 C29 C3
## 66498 31614 44242 48813 48586 64641 62939 47603 50588 54901 71429
## C30 C31 C32 C33 C34 C35 C36 C37 C38 C39 C4
## 50985 36004 42673 18624 51048 54782 47082 51279 47484 50040 43049
## C40 C5 C6 C7 C8 C9 L1 L10 L11 L12 L13
## 45373 52582 64638 40623 35523 55198 59489 29140 38500 46667 10614
## L14 L15 L16 L17 L18 L19 L2 L20 L21 L22 L23
## 48171 127935 173 127168 36745 148778 112775 74426 34030 27094 41554
## L24 L3 L4 L5 L6 L7 L8 L9 N1 N2 P1
## 54327 49541 65420 51715 140335 54744 49721 56880 6317 470 399827Looking in to the number of sequences into each samples: Samples for Argentine stem weevils: A17, A19, A22, A24, A25, A29, A18, A20, A21, A23 has sequences <2000 others samples have sequences `>1000
For CRW samples we need to remove C16, similarly L16 from lucerne weevil sample: both with <1000 So we decided to remove these samples before rarefaction, so that we don’t loose much information for other samples. Estimation of the abundance of differnet taxa within a sample are therefore relative, and it is necessary to normalize counts before comparing a single taxon between samples.
Before we proceed further, we will also be removing Negative, positive and parasite samples, to just get samples from weevils:
To get the subsample:
OTU <- subset(OTU_all, select = -c(A17,A19,A22,A24,A25,A29,C16,L16,A18,A20,A21,A23,N1,N2,P1,C13,C17,C18,C19,C20,C21,C33,L13,L17,A15,A35))The total number of sequences for each sample from the subset
colSums(OTU)## A1 A10 A11 A12 A13 A16 A2 A26 A27 A28 A3
## 180344 83627 69685 65585 78971 553080 58335 59016 64992 220645 77216
## A30 A31 A32 A33 A34 A4 A5 A6 A7 A8 A9
## 142596 54753 104439 67731 325583 104809 219435 58010 59057 205737 302729
## C1 C10 C11 C12 C2 C22 C23 C24 C25 C26 C27
## 36845 55258 74592 68346 46334 44242 48813 48586 64641 62939 47603
## C28 C29 C3 C30 C31 C32 C34 C35 C36 C37 C38
## 50588 54901 71429 50985 36004 42673 51048 54782 47082 51279 47484
## C39 C4 C40 C5 C6 C7 C8 C9 L1 L10 L11
## 50040 43049 45373 52582 64638 40623 35523 55198 59489 29140 38500
## L12 L14 L15 L18 L19 L2 L20 L21 L22 L23 L24
## 46667 48171 127935 36745 148778 112775 74426 34030 27094 41554 54327
## L3 L4 L5 L6 L7 L8 L9
## 49541 65420 51715 140335 54744 49721 56880The sample with minimum number of sequences
min(colSums(OTU))## [1] 27094Plot a histogram of the number of sequences for each sample
hist(colSums(OTU), breaks = 20)
Now we will import the taxonomy table. This is the output from the Sintax analysis.
import_taxa <- read.table('raw_data/sintax_taxonomy.tsv',header=TRUE,sep='\t', row.names=1)
head(import_taxa)## Kingdom Phylum Class
## 0tu.1 Bacteria Proteobacteria Gammaproteobacteria
## 0tu.10 Bacteria Actinobacteria Actinobacteria
## 0tu.100 Bacteria Cyanobacteria/Chloroplast Chloroplast
## 0tu.1002 Bacteria Proteobacteria Alphaproteobacteria
## 0tu.1007 Bacteria Actinobacteria Actinobacteria
## 0tu.101 Bacteria Proteobacteria Alphaproteobacteria
## Order Family Genus Confidence
## 0tu.1 Enterobacteriales Enterobacteriaceae Buchnera 0.27
## 0tu.10 Actinomycetales Nocardiaceae Rhodococcus 1.00
## 0tu.100 Chloroplast Streptophyta NA
## 0tu.1002 Rhizobiales Methylobacteriaceae Methylobacterium 1.00
## 0tu.1007 Actinomycetales Pseudonocardiaceae Umezawaea 0.66
## 0tu.101 Rhizobiales Brucellaceae Ochrobactrum 1.00Convert to a matrix
taxonomy <- as.matrix(import_taxa)Create a taxonomy class object
TAX <- tax_table(taxonomy)Import the sample metadata
metadata <- read.table('raw_data/sample_metadata_complete.tsv',header = T,sep='\t',row.names = 1)
metadata## Name Parasitization Sex Site No.of.Parasites
## N1 Neg.qPCR - - - NA
## N2 Neg.Control - - - NA
## C1 Crw NP M Rakaia NA
## C2 Crw NP F Rakaia NA
## C3 Crw NP F Rakaia NA
## C4 Crw NP M Rakaia NA
## C5 Crw NP M Rakaia NA
## C6 Crw NP M Rakaia NA
## C7 Crw NP M Rakaia NA
## C8 Crw NP M Rakaia NA
## C9 Crw NP M Rakaia NA
## C10 Crw P M Rakaia NA
## C11 Crw P F Rakaia NA
## C12 Crw P F Rakaia NA
## C13 Par.Crw - - Rakaia 1
## C16 Par.Crw - - Rakaia 1
## C17 Par.Crw - - Rakaia 1
## C18 Par.Crw - - Rakaia 1
## C19 Par.Crw - - Rakaia 1
## C20 Par.Crw - - Rakaia 1
## C21 Par.Crw - - Rakaia 4
## C22 Crw P F Lincoln NA
## C23 Crw P F Lincoln NA
## C24 Crw P F Lincoln NA
## C25 Crw P M Lincoln NA
## C26 Crw NP F Lincoln NA
## C27 Crw NP M Lincoln NA
## C28 Crw NP F Lincoln NA
## C29 Crw NP F Lincoln NA
## C30 Crw NP F Lincoln NA
## C31 Crw P F Lincoln NA
## C32 Crw P M Lincoln NA
## C33 Par.Crw - - Lincoln 2
## C34 Crw NP M Lincoln NA
## C35 Crw P M Lincoln NA
## C36 Crw P F Lincoln NA
## C37 Crw P F Lincoln NA
## C38 Crw P M Lincoln NA
## C39 Crw P M Lincoln NA
## C40 Crw P F Lincoln NA
## P1 Pos.Control - - - NA
## L1 Lw NP F Lincoln NA
## L2 Lw NP M Lincoln NA
## L3 Lw NP F Lincoln NA
## L4 Lw NP F Lincoln NA
## L5 Lw P M Lincoln NA
## L6 Lw P M Lincoln NA
## L7 Lw NP F Lincoln NA
## L8 Lw P M Lincoln NA
## L9 Lw NP M Lincoln NA
## L10 Lw NP F Lincoln NA
## L11 Lw NP F Lincoln NA
## L12 Lw NP M Lincoln NA
## L13 Par.Lw - - Lincoln 1
## L14 Lw P M Lincoln NA
## L15 Lw P M Lincoln NA
## L16 Lw P M Lincoln NA
## L17 Par.Lw - - Lincoln 1
## L18 Lw NP F Grasmere NA
## L19 Lw NP F Grasmere NA
## L20 Lw NP F Grasmere NA
## L21 Lw NP F Grasmere NA
## L22 Lw NP M Grasmere NA
## L23 Lw NP M Grasmere NA
## L24 Lw P M Grasmere NA
## A1 Asw NP M Lincoln NA
## A2 Asw NP M Lincoln NA
## A3 Asw NP M Lincoln NA
## A4 Asw P M Lincoln NA
## A5 Asw NP M Lincoln NA
## A6 Asw NP F Lincoln NA
## A7 Asw NP F Lincoln NA
## A8 Asw NP F Lincoln NA
## A9 Asw P F Lincoln NA
## A10 Asw NP F Lincoln NA
## A11 Asw NP F Lincoln NA
## A12 Asw P M Lincoln NA
## A13 Asw NP F Lincoln NA
## A15 Par.Asw - - Lincoln 1
## A16 Asw P F Lincoln NA
## A17 Asw P M Lincoln NA
## A18 Asw P M Lincoln NA
## A19 Par.Asw - - Lincoln 1
## A20 Par.Asw - - Lincoln 2
## A21 Asw NP M Lincoln NA
## A22 Asw NP F Lincoln NA
## A23 Asw P M Lincoln NA
## A24 Asw P M Lincoln NA
## A25 Asw P F Lincoln NA
## A26 Asw P M Lincoln NA
## A27 Asw P M Lincoln NA
## A28 Asw P M Lincoln NA
## A29 Par.Asw - - Lincoln 3
## A30 Asw NP M Lincoln NA
## A31 Asw NP F Lincoln NA
## A32 Asw P M Lincoln NA
## A33 Asw P - Lincoln NA
## A34 Asw P M Lincoln NA
## A35 Par.Asw - - Lincoln 3We need to remove the samples that we removed earlier from the OTU table, and we also need to remove column with No.of parasites
meta_data_weevils <- subset(metadata, select = -c(No.of.Parasites))
meta_data_weevils## Name Parasitization Sex Site
## N1 Neg.qPCR - - -
## N2 Neg.Control - - -
## C1 Crw NP M Rakaia
## C2 Crw NP F Rakaia
## C3 Crw NP F Rakaia
## C4 Crw NP M Rakaia
## C5 Crw NP M Rakaia
## C6 Crw NP M Rakaia
## C7 Crw NP M Rakaia
## C8 Crw NP M Rakaia
## C9 Crw NP M Rakaia
## C10 Crw P M Rakaia
## C11 Crw P F Rakaia
## C12 Crw P F Rakaia
## C13 Par.Crw - - Rakaia
## C16 Par.Crw - - Rakaia
## C17 Par.Crw - - Rakaia
## C18 Par.Crw - - Rakaia
## C19 Par.Crw - - Rakaia
## C20 Par.Crw - - Rakaia
## C21 Par.Crw - - Rakaia
## C22 Crw P F Lincoln
## C23 Crw P F Lincoln
## C24 Crw P F Lincoln
## C25 Crw P M Lincoln
## C26 Crw NP F Lincoln
## C27 Crw NP M Lincoln
## C28 Crw NP F Lincoln
## C29 Crw NP F Lincoln
## C30 Crw NP F Lincoln
## C31 Crw P F Lincoln
## C32 Crw P M Lincoln
## C33 Par.Crw - - Lincoln
## C34 Crw NP M Lincoln
## C35 Crw P M Lincoln
## C36 Crw P F Lincoln
## C37 Crw P F Lincoln
## C38 Crw P M Lincoln
## C39 Crw P M Lincoln
## C40 Crw P F Lincoln
## P1 Pos.Control - - -
## L1 Lw NP F Lincoln
## L2 Lw NP M Lincoln
## L3 Lw NP F Lincoln
## L4 Lw NP F Lincoln
## L5 Lw P M Lincoln
## L6 Lw P M Lincoln
## L7 Lw NP F Lincoln
## L8 Lw P M Lincoln
## L9 Lw NP M Lincoln
## L10 Lw NP F Lincoln
## L11 Lw NP F Lincoln
## L12 Lw NP M Lincoln
## L13 Par.Lw - - Lincoln
## L14 Lw P M Lincoln
## L15 Lw P M Lincoln
## L16 Lw P M Lincoln
## L17 Par.Lw - - Lincoln
## L18 Lw NP F Grasmere
## L19 Lw NP F Grasmere
## L20 Lw NP F Grasmere
## L21 Lw NP F Grasmere
## L22 Lw NP M Grasmere
## L23 Lw NP M Grasmere
## L24 Lw P M Grasmere
## A1 Asw NP M Lincoln
## A2 Asw NP M Lincoln
## A3 Asw NP M Lincoln
## A4 Asw P M Lincoln
## A5 Asw NP M Lincoln
## A6 Asw NP F Lincoln
## A7 Asw NP F Lincoln
## A8 Asw NP F Lincoln
## A9 Asw P F Lincoln
## A10 Asw NP F Lincoln
## A11 Asw NP F Lincoln
## A12 Asw P M Lincoln
## A13 Asw NP F Lincoln
## A15 Par.Asw - - Lincoln
## A16 Asw P F Lincoln
## A17 Asw P M Lincoln
## A18 Asw P M Lincoln
## A19 Par.Asw - - Lincoln
## A20 Par.Asw - - Lincoln
## A21 Asw NP M Lincoln
## A22 Asw NP F Lincoln
## A23 Asw P M Lincoln
## A24 Asw P M Lincoln
## A25 Asw P F Lincoln
## A26 Asw P M Lincoln
## A27 Asw P M Lincoln
## A28 Asw P M Lincoln
## A29 Par.Asw - - Lincoln
## A30 Asw NP M Lincoln
## A31 Asw NP F Lincoln
## A32 Asw P M Lincoln
## A33 Asw P - Lincoln
## A34 Asw P M Lincoln
## A35 Par.Asw - - Lincoln# making vector of row names that needs to be removed
row_names_to_remove <- c("A17","A19","A22","A24","A25","A29","C16","L16","A18","A20","A21","A23","N1","N2","P1","C13","C17","C18","C19","C20","C21","C33","L13","L17","A15","A35")
# removing the specified rows:
meta_data_weevils[!(row.names(meta_data_weevils) %in% row_names_to_remove), ]## Name Parasitization Sex Site
## C1 Crw NP M Rakaia
## C2 Crw NP F Rakaia
## C3 Crw NP F Rakaia
## C4 Crw NP M Rakaia
## C5 Crw NP M Rakaia
## C6 Crw NP M Rakaia
## C7 Crw NP M Rakaia
## C8 Crw NP M Rakaia
## C9 Crw NP M Rakaia
## C10 Crw P M Rakaia
## C11 Crw P F Rakaia
## C12 Crw P F Rakaia
## C22 Crw P F Lincoln
## C23 Crw P F Lincoln
## C24 Crw P F Lincoln
## C25 Crw P M Lincoln
## C26 Crw NP F Lincoln
## C27 Crw NP M Lincoln
## C28 Crw NP F Lincoln
## C29 Crw NP F Lincoln
## C30 Crw NP F Lincoln
## C31 Crw P F Lincoln
## C32 Crw P M Lincoln
## C34 Crw NP M Lincoln
## C35 Crw P M Lincoln
## C36 Crw P F Lincoln
## C37 Crw P F Lincoln
## C38 Crw P M Lincoln
## C39 Crw P M Lincoln
## C40 Crw P F Lincoln
## L1 Lw NP F Lincoln
## L2 Lw NP M Lincoln
## L3 Lw NP F Lincoln
## L4 Lw NP F Lincoln
## L5 Lw P M Lincoln
## L6 Lw P M Lincoln
## L7 Lw NP F Lincoln
## L8 Lw P M Lincoln
## L9 Lw NP M Lincoln
## L10 Lw NP F Lincoln
## L11 Lw NP F Lincoln
## L12 Lw NP M Lincoln
## L14 Lw P M Lincoln
## L15 Lw P M Lincoln
## L18 Lw NP F Grasmere
## L19 Lw NP F Grasmere
## L20 Lw NP F Grasmere
## L21 Lw NP F Grasmere
## L22 Lw NP M Grasmere
## L23 Lw NP M Grasmere
## L24 Lw P M Grasmere
## A1 Asw NP M Lincoln
## A2 Asw NP M Lincoln
## A3 Asw NP M Lincoln
## A4 Asw P M Lincoln
## A5 Asw NP M Lincoln
## A6 Asw NP F Lincoln
## A7 Asw NP F Lincoln
## A8 Asw NP F Lincoln
## A9 Asw P F Lincoln
## A10 Asw NP F Lincoln
## A11 Asw NP F Lincoln
## A12 Asw P M Lincoln
## A13 Asw NP F Lincoln
## A16 Asw P F Lincoln
## A26 Asw P M Lincoln
## A27 Asw P M Lincoln
## A28 Asw P M Lincoln
## A30 Asw NP M Lincoln
## A31 Asw NP F Lincoln
## A32 Asw P M Lincoln
## A33 Asw P - Lincoln
## A34 Asw P M LincolnCreate a Phyloseq sample_data-class
META <- sample_data(meta_data_weevils)Create a Phyloseq object
Now that we have all the components, it is time to create a Phyloseq object
physeq <- phyloseq(OTU,TAX,META)
physeq## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1142 taxa and 73 samples ]
## sample_data() Sample Data: [ 73 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 1142 taxa by 7 taxonomic ranks ]If we had metadata variables as numerics, we need to convert them as characters, but in our case, we got them as characters, so no modifications needed.
Looking at the data
Now that we have our Phyloseq object, we will take a look at it. One of the first steps is to check alpha rarefaction of species richness. This is done to show that there has been sufficient sequencing to detect most species (OTUs).
rarecurve(t(otu_table(physeq)), step=50, cex=1)
create a bar plot of abundance
plot_bar(physeq, fill="Kingdom")
print(min(sample_sums(physeq)))## [1] 27094print(max(sample_sums(physeq)))## [1] 553080Rarefy the data
We can use rarefaction to simulate an even number of reads per sample. Rarefying the data is preferred for some analyses, though there is some debate. We will create a rarefied version of the Phyloseq object.
we will rarefy the data around 90% of the lowest sample
physeq.rarefied <- rarefy_even_depth(physeq, rngseed=1, sample.size=0.9*min(sample_sums(physeq)), replace=F)## `set.seed(1)` was used to initialize repeatable random subsampling.## Please record this for your records so others can reproduce.## Try `set.seed(1); .Random.seed` for the full vector## ...## 20OTUs were removed because they are no longer
## present in any sample after random subsampling## ...now plot the rerefied version
plot_bar(physeq.rarefied)
# Saving work to files You can save the Phyloseq object you just created, and then import it into another R session later. This way you do not have to re-import all the components separately.
Also, below are a couple of examples of saving graphs. There are many options for this that you can explore to create publication-quality graphics of your results
Save the phyloseq object
saveRDS(physeq, 'weevils_phyloseq.rds')Also save the rarefied version
saveRDS(physeq.rarefied, 'weevils_phyloseq_rarefied.rds')#Saving a graph to file
# open a pdf file
pdf('species_richness_plot.pdf')
# run the plot, or add the saved one
rarecurve(t(otu_table(physeq)), step=50, cex=1.5, col='blue',lty=2)
# close the pdf
dev.off()## quartz_off_screen
## 2Saving in jpeg
jpeg("species_richness_plot.jpg", width = 800, height = 800)
rarecurve(t(otu_table(physeq)), step=50, cex=1.5, col='blue',lty=2)
dev.off()## quartz_off_screen
## 2Resetting the graphics device, because the plot was not displayed properly
dev.off()## null device
## 1Plotting taxonomy
plot_bar(physeq, fill='Phylum')
plot_bar(physeq.rarefied, fill='Phylum')
Now add ggplot facet_wrap tools to graph
barplot1 <- plot_bar(physeq.rarefied, fill='Phylum')
barplot1 +facet_wrap(~Name, scales="free_x", nrow = 1)
Combine visuals to present more information from non rarefied data
barplot2 <- plot_bar(physeq, x="Parasitization", fill = 'Phylum') + facet_wrap(~Name, scales = "free_x", nrow = 1)
barplot2
barplot3 <- plot_bar(physeq.rarefied, x="Parasitization", fill = 'Phylum') + facet_wrap(~Name, scales = "free_x", nrow = 1)
barplot3
barplot4 = plot_bar(physeq.rarefied, "Family", fill="Genus",facet_grid=~Name)
barplot4+geom_point(aes(x=Family, y=Abundance), color="black", position = "jitter", size=3)
Community ecology
jac_dist <- distance(physeq.rarefied, method = "jaccard", binary = TRUE)
jac_dist## A1 A10 A11 A12 A13 A16 A2
## A10 0.7915408
## A11 0.8403909 0.7034483
## A12 0.9240506 0.7067669 0.6804124
## A13 0.6746988 0.6467662 0.6588235 0.7954545
## A16 0.5991649 0.7549505 0.8379747 0.8841310 0.6788321
## A2 0.7313916 0.7415730 0.7500000 0.8450704 0.7488152 0.7956204
## A26 0.6106719 0.7950450 0.8581395 0.9080460 0.7222222 0.4821092 0.8130631
## A27 0.6795096 0.7917526 0.8684760 0.9064449 0.7302231 0.5325342 0.8299595
## A28 0.7012448 0.8475336 0.9001513 0.9324324 0.7895522 0.5706447 0.8679525
## A3 0.5822785 0.7711599 0.8243243 0.8821549 0.6829268 0.6216216 0.7242525
## A30 0.6323851 0.7270195 0.8063584 0.8559078 0.6603774 0.5584158 0.7994652
## A31 0.6732892 0.7412791 0.8103976 0.8558282 0.6750700 0.5454545 0.8128492
## A32 0.6129707 0.7436548 0.8193717 0.8788660 0.6455696 0.4980392 0.7763819
## A33 0.6498674 0.6884615 0.7827869 0.8340249 0.6094891 0.5767442 0.7433962
## A34 0.6973501 0.8393939 0.8960245 0.9270517 0.7879699 0.5536960 0.8584337
## A4 0.7971831 0.6919192 0.7714286 0.7716049 0.6943231 0.7759815 0.7696078
## A5 0.6112150 0.7945493 0.8580645 0.8972163 0.7160752 0.4701627 0.8236515
## A6 0.8553459 0.6575342 0.6666667 0.6923077 0.7103825 0.8295739 0.7297297
## A7 0.6426513 0.6289593 0.7050000 0.7474227 0.5588235 0.6135266 0.7229437
## A8 0.6474501 0.7262248 0.8083832 0.8630952 0.6460674 0.5796813 0.7944444
## A9 0.6623377 0.7500000 0.8163265 0.8728324 0.6863271 0.5103306 0.7923497
## C1 0.7993921 0.7102273 0.7379310 0.8716216 0.7695853 0.8171021 0.7341040
## C10 0.6889535 0.7695652 0.7354497 0.8905473 0.6485356 0.7175926 0.7612613
## C11 0.8535826 0.7804878 0.7812500 0.8333333 0.7878788 0.8595642 0.8000000
## C12 0.6940299 0.7689655 0.8098859 0.9160584 0.6983607 0.6751055 0.7624113
## C2 0.7333333 0.7604563 0.7844828 0.8962656 0.7021277 0.7188841 0.7480315
## C22 0.7478006 0.7840376 0.7784091 0.8944444 0.7056277 0.7587822 0.7756098
## C23 0.7470238 0.7222222 0.7559524 0.8843931 0.6977778 0.7670588 0.7435897
## C24 0.7836991 0.7709497 0.6766917 0.8571429 0.6934673 0.7995110 0.7380952
## C25 0.7902736 0.7894737 0.7432432 0.8888889 0.7323944 0.7927711 0.7868852
## C26 0.7545455 0.7210526 0.7484277 0.8562500 0.6837209 0.7990654 0.7158470
## C27 0.7571429 0.7578947 0.8257576 0.9029851 0.7207792 0.7474950 0.7509025
## C28 0.7185355 0.7826087 0.8400000 0.9114754 0.7294118 0.7000000 0.7924528
## C29 0.7979003 0.8016529 0.8173077 0.9150943 0.7777778 0.8089172 0.8050847
## C3 0.7120743 0.7661692 0.7407407 0.8742515 0.6666667 0.7553957 0.7301587
## C30 0.7806122 0.7500000 0.8230088 0.9082969 0.7364621 0.7845188 0.7773279
## C31 0.7881137 0.7813765 0.8264840 0.9095023 0.7509158 0.7983193 0.7740586
## C32 0.7602180 0.7575758 0.7970297 0.9138756 0.7226562 0.7789934 0.7544643
## C34 0.7469880 0.7862069 0.8464419 0.9225092 0.7555556 0.7439516 0.7972028
## C35 0.7653061 0.7704280 0.8275862 0.8965517 0.7330961 0.7745303 0.8015564
## C36 0.8025316 0.7651822 0.8295964 0.9111111 0.7678571 0.7899160 0.8072289
## C37 0.7493857 0.7689531 0.8167331 0.9108527 0.7342193 0.7355372 0.7528090
## C38 0.7570332 0.7722008 0.8340426 0.9159664 0.7349823 0.7626050 0.7649402
## C39 0.7870130 0.7745902 0.8248848 0.9136364 0.7627737 0.7974684 0.7824268
## C4 0.7002584 0.7453184 0.8073770 0.9087302 0.7113402 0.7023555 0.8065693
## C40 0.7443038 0.7761194 0.8360656 0.9280000 0.7440273 0.7572614 0.7692308
## C5 0.9084967 0.8472222 0.7676768 0.8089888 0.8681319 0.9156328 0.8208955
## C6 0.8636364 0.8181818 0.6981132 0.8380952 0.8128342 0.8814815 0.8082192
## C7 0.7981651 0.7430168 0.7517241 0.8541667 0.7320574 0.8329412 0.7600000
## C8 0.7241379 0.7397260 0.7459459 0.8750000 0.6379310 0.7129412 0.7298578
## C9 0.7088235 0.7625571 0.7795699 0.8835979 0.6822034 0.7365967 0.7476190
## L1 0.7578348 0.7358491 0.7759563 0.8756757 0.7058824 0.7580645 0.7735849
## L10 0.7049180 0.7816456 0.8241379 0.8907850 0.7121212 0.6774194 0.7916667
## L11 0.6768868 0.7523810 0.8405316 0.8900000 0.6957831 0.6195426 0.7968750
## L12 0.7020057 0.7219731 0.7512953 0.8461538 0.6514523 0.7276888 0.7293578
## L14 0.6643836 0.7947977 0.8452012 0.9181818 0.7053824 0.6376238 0.8040936
## L15 0.7345133 0.8047337 0.8498403 0.9113924 0.7055394 0.6893204 0.8214286
## L18 0.6597222 0.7620482 0.8312102 0.8962264 0.6812865 0.6222222 0.7713415
## L19 0.6415094 0.7673716 0.8258065 0.8881789 0.6784661 0.6282828 0.7692308
## L2 0.6837782 0.7964377 0.8590426 0.9160105 0.7238806 0.6171004 0.8076923
## L20 0.6431818 0.7983193 0.8368580 0.8985075 0.6862745 0.6301370 0.8039773
## L21 0.6338384 0.7770492 0.8333333 0.8861210 0.6762821 0.6721649 0.7627119
## L22 0.6268344 0.7915633 0.8493506 0.9051282 0.7146341 0.5910781 0.7964824
## L23 0.6725441 0.7620690 0.8157895 0.9007353 0.6744186 0.6680761 0.7727273
## L24 0.6893939 0.7896552 0.8282443 0.9018868 0.6835017 0.6849894 0.7836879
## L3 0.6895787 0.8016997 0.8374233 0.9131737 0.7384196 0.6365422 0.8108883
## L4 0.6516588 0.7811550 0.8446602 0.9035370 0.6952663 0.6257669 0.7870370
## L5 0.6837782 0.8085642 0.8650794 0.9160105 0.7395577 0.6348624 0.8107417
## L6 0.7229730 0.7872340 0.8436482 0.8957655 0.7088235 0.6914062 0.8079268
## L7 0.7086420 0.7673611 0.8084291 0.8867925 0.6920530 0.6793249 0.7867133
## L8 0.7123656 0.7658730 0.8080357 0.9039301 0.7374101 0.7066667 0.7880000
## L9 0.7951070 0.7444444 0.7619048 0.8472222 0.7272727 0.8138425 0.7471264
## A26 A27 A28 A3 A30 A31 A32
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27 0.5450082
## A28 0.5287671 0.4567901
## A3 0.6318898 0.6558559 0.6850282
## A30 0.5468451 0.5543672 0.6050420 0.6409692
## A31 0.5736434 0.5425926 0.5974026 0.6430206 0.5410200
## A32 0.4857685 0.5157343 0.5578801 0.6122881 0.5000000 0.5408805
## A33 0.6060606 0.6372549 0.7077827 0.6781003 0.6093366 0.6066838 0.5788235
## A34 0.5152778 0.4741144 0.3051948 0.6676259 0.6002825 0.6005789 0.5341880
## A4 0.8067941 0.7956778 0.8501441 0.7890173 0.7589744 0.7700535 0.7688679
## A5 0.4208633 0.5347003 0.4986523 0.6235955 0.5714286 0.5787546 0.5035714
## A6 0.8555046 0.8589212 0.8947368 0.8289474 0.8005698 0.8149254 0.8139535
## A7 0.6533333 0.6807229 0.7507418 0.6540698 0.5957447 0.6191781 0.5885287
## A8 0.5562016 0.5579710 0.6298883 0.6318182 0.5144766 0.5805740 0.5095137
## A9 0.5206287 0.5719361 0.5931721 0.6414254 0.5576923 0.5786026 0.5359343
## C1 0.8248337 0.8215010 0.8601190 0.7753165 0.7783784 0.7806268 0.7901235
## C10 0.7205240 0.7285429 0.7767857 0.6776119 0.7040816 0.6703297 0.6937799
## C11 0.8671171 0.8670757 0.9035608 0.8424437 0.8387978 0.8343023 0.8567901
## C12 0.6530612 0.6534091 0.6919708 0.6878173 0.6620690 0.6423358 0.6373626
## C2 0.7215447 0.6897881 0.7279412 0.7142857 0.6880952 0.6758794 0.7054945
## C22 0.7648352 0.7933071 0.8316252 0.7640118 0.7329843 0.7362637 0.7421687
## C23 0.7589286 0.7811245 0.8266667 0.7292308 0.7486911 0.7561644 0.7445255
## C24 0.8032037 0.8213552 0.8671642 0.7898089 0.7774725 0.7936963 0.7894737
## C25 0.8129176 0.8178138 0.8556548 0.7812500 0.7768817 0.7720798 0.8004866
## C26 0.7839644 0.8012048 0.8483063 0.7327044 0.7553191 0.7492958 0.7658537
## C27 0.7485714 0.7578947 0.7800546 0.7150000 0.7274725 0.7386364 0.7356557
## C28 0.7033582 0.7204117 0.7503356 0.7102804 0.7199170 0.6938326 0.7029703
## C29 0.8092369 0.8161765 0.8437936 0.7804878 0.7939110 0.8024390 0.7899344
## C3 0.7645740 0.7862903 0.8288690 0.7047619 0.7319035 0.7280453 0.7413793
## C30 0.7813121 0.7949183 0.8289655 0.7533156 0.7558140 0.7716346 0.7624190
## C31 0.7944112 0.7981651 0.8317107 0.7673797 0.7681499 0.7932692 0.7658643
## C32 0.7784679 0.7836812 0.8267045 0.7443820 0.7481663 0.7519182 0.7471526
## C34 0.7523810 0.7548501 0.7744154 0.7389163 0.7456522 0.7431818 0.7293388
## C35 0.7862745 0.7795993 0.8190608 0.7571802 0.7534562 0.7572115 0.7494600
## C36 0.7936508 0.7908257 0.8245125 0.7730871 0.7587822 0.7628362 0.7543860
## C37 0.7470817 0.7454874 0.7743733 0.7020725 0.7194570 0.7306792 0.7066381
## C38 0.7702970 0.7737226 0.8030726 0.7454068 0.7659091 0.7613365 0.7451404
## C39 0.8055556 0.8083942 0.8328691 0.7792553 0.8036530 0.7834550 0.7782609
## C4 0.7162978 0.6950758 0.7240876 0.6835106 0.6602871 0.6600000 0.6971678
## C40 0.7627451 0.7600000 0.7891061 0.7455013 0.7656250 0.7523585 0.7371795
## C5 0.9267735 0.9211618 0.9414414 0.8986486 0.9047619 0.8945783 0.9139241
## C6 0.8901602 0.8925620 0.9206587 0.8561873 0.8631285 0.8635015 0.8847118
## C7 0.8266667 0.8231707 0.8647845 0.7816456 0.7903226 0.7723343 0.7920792
## C8 0.7302632 0.7374749 0.7762763 0.6985075 0.6815789 0.6750000 0.7074341
## C9 0.7494553 0.7648221 0.8157895 0.7057057 0.7249357 0.6925208 0.7285714
## L1 0.7532751 0.7779961 0.8236152 0.7108434 0.7194805 0.7459893 0.7476415
## L10 0.6536204 0.6779964 0.7219917 0.6674817 0.6104784 0.6651481 0.6242038
## L11 0.6217822 0.6442831 0.6873239 0.6512195 0.6174497 0.6250000 0.6160338
## L12 0.7217391 0.7592233 0.8057971 0.6873156 0.6855670 0.7040000 0.6921241
## L14 0.6259542 0.6305506 0.6795580 0.6327014 0.5951860 0.6447368 0.6262626
## L15 0.6907407 0.6931034 0.7222222 0.6853147 0.6531049 0.6961207 0.6895874
## L18 0.6108949 0.6166365 0.6694678 0.6207729 0.5835189 0.6308725 0.5987526
## L19 0.6405354 0.6513274 0.6919890 0.6246973 0.6087912 0.6283784 0.6107660
## L2 0.6093190 0.5887372 0.6002766 0.6474359 0.5898990 0.6188525 0.6007605
## L20 0.6266417 0.6382609 0.6648276 0.6302326 0.6260504 0.6567164 0.6297030
## L21 0.6587302 0.6512059 0.7136812 0.5973684 0.6252874 0.6620047 0.6173913
## L22 0.5531136 0.6053068 0.5906593 0.5873362 0.5731463 0.6240000 0.5822306
## L23 0.6653226 0.6820702 0.7262411 0.6122995 0.6450116 0.6761229 0.6299559
## L24 0.6841046 0.6992620 0.7339972 0.6300268 0.6350711 0.7042254 0.6593886
## L3 0.6455224 0.6296296 0.6601671 0.6719818 0.6357895 0.6405229 0.6549020
## L4 0.6250000 0.6347197 0.6685552 0.6283619 0.5900901 0.6287016 0.6166667
## L5 0.6433566 0.6216667 0.6136986 0.6474359 0.6012024 0.6438632 0.5980952
## L6 0.6729679 0.6407942 0.7044199 0.6722090 0.6493506 0.6754967 0.6350515
## L7 0.6787149 0.6942910 0.7372881 0.6588542 0.6157518 0.6793349 0.6594360
## L8 0.7073684 0.7429112 0.7752161 0.6769663 0.7021792 0.7103275 0.7158837
## L9 0.8112360 0.8187373 0.8716814 0.7628205 0.7744565 0.7834758 0.7775000
## A33 A34 A4 A5 A6 A7 A8
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34 0.7056213
## A4 0.7397260 0.8440233
## A5 0.6069246 0.4911565 0.7935872
## A6 0.7551020 0.8972810 0.7109827 0.8507463
## A7 0.5594406 0.7394578 0.7209302 0.6569647 0.7224880
## A8 0.6085859 0.6116643 0.7255435 0.5596330 0.7988166 0.5983607
## A9 0.5592784 0.5759768 0.7610390 0.5177570 0.8171429 0.6180371 0.5745140
## C1 0.7647059 0.8641791 0.7962085 0.8295688 0.7181208 0.7372881 0.7724719
## C10 0.7062706 0.7878338 0.8122605 0.7504990 0.8125000 0.6590909 0.6711590
## C11 0.8470149 0.8995502 0.7925532 0.8804124 0.8029197 0.8008850 0.8418079
## C12 0.6798867 0.6877761 0.7886435 0.6842105 0.8284672 0.6749226 0.6563981
## C2 0.7205882 0.7445255 0.7827586 0.7281369 0.8306452 0.7316294 0.7053140
## C22 0.7651007 0.8199405 0.8033473 0.7838384 0.8074866 0.7058824 0.7324324
## C23 0.7253521 0.8271237 0.7826087 0.7886179 0.7740113 0.6987952 0.7349727
## C24 0.7584906 0.8627451 0.7951220 0.8270833 0.7397260 0.7123894 0.7749288
## C25 0.7761733 0.8545727 0.7990654 0.8209877 0.7875000 0.7458333 0.7945205
## C26 0.7561837 0.8420268 0.8053097 0.8089431 0.7530120 0.6970954 0.7486188
## C27 0.7486486 0.7802198 0.8056426 0.7531306 0.8134328 0.7681159 0.7438753
## C28 0.7251908 0.7520216 0.8163842 0.7105263 0.8441558 0.7418478 0.7299578
## C29 0.7872340 0.8410689 0.8388278 0.8079096 0.8294931 0.8006645 0.8123515
## C3 0.7340426 0.8258258 0.8146552 0.7738589 0.8089888 0.6708333 0.7382920
## C30 0.7566766 0.8294036 0.8257840 0.7996324 0.8034934 0.7827476 0.7793427
## C31 0.7797619 0.8337989 0.8115942 0.8051948 0.8274336 0.7922078 0.7862233
## C32 0.7468354 0.8271429 0.8195489 0.7884615 0.7990431 0.7526132 0.7667494
## C34 0.7574124 0.7779310 0.8302469 0.7500000 0.8296296 0.7879656 0.7702407
## C35 0.7500000 0.8227147 0.8090278 0.7886029 0.8135593 0.7596154 0.7710280
## C36 0.7823529 0.8199719 0.8226950 0.8000000 0.8097345 0.7870968 0.7767221
## C37 0.7500000 0.7693390 0.8167203 0.7540984 0.8223938 0.7515152 0.7391304
## C38 0.7658960 0.8067227 0.8184932 0.7690875 0.8298755 0.7843750 0.7720930
## C39 0.7964602 0.8365922 0.8315412 0.7977528 0.8153153 0.8064516 0.8079625
## C4 0.7055394 0.7332362 0.7933333 0.7279550 0.8274510 0.7111111 0.6707317
## C40 0.7549858 0.7893258 0.8322368 0.7642726 0.8458498 0.7945619 0.7744875
## C5 0.9087302 0.9377845 0.8713450 0.9284211 0.8073394 0.8605769 0.9069767
## C6 0.8498024 0.9151515 0.8125000 0.8924051 0.8032787 0.8009479 0.8470588
## C7 0.7898551 0.8637725 0.7766990 0.8312757 0.7565789 0.7510549 0.7746479
## C8 0.6949153 0.7837838 0.7530864 0.7520161 0.7817259 0.6653846 0.6864865
## C9 0.7205387 0.8109306 0.8221344 0.7695391 0.8071066 0.6896552 0.7342105
## L1 0.7348993 0.8136095 0.7759336 0.7948718 0.7979275 0.7110266 0.7253333
## L10 0.7135417 0.7110799 0.7515152 0.6787659 0.8169492 0.6820809 0.6065574
## L11 0.6613333 0.6851064 0.7790698 0.6470588 0.8446602 0.6550725 0.6295455
## L12 0.6778523 0.8095930 0.7160494 0.7514911 0.7733990 0.6666667 0.6693548
## L14 0.7201946 0.6624473 0.7934783 0.6452763 0.8727811 0.6775068 0.6219780
## L15 0.7395577 0.7185792 0.7614943 0.7076125 0.8606811 0.7333333 0.6767241
## L18 0.6632391 0.6633663 0.7933884 0.6487455 0.8390093 0.6648199 0.6169265
## L19 0.6743590 0.6952909 0.7427746 0.6541219 0.8190476 0.6544944 0.5986395
## L2 0.7066667 0.6243169 0.7745098 0.6262626 0.8619792 0.7169811 0.6004098
## L20 0.7098321 0.6512605 0.7675676 0.6573913 0.8508772 0.7012987 0.6017505
## L21 0.6937669 0.7152975 0.7692308 0.6910420 0.8498294 0.6746269 0.6378505
## L22 0.6636771 0.6013699 0.7905882 0.5664940 0.8524173 0.6808511 0.6143141
## L23 0.7247956 0.7169540 0.7628205 0.7000000 0.8505338 0.6687307 0.6289157
## L24 0.7042254 0.7247839 0.7884615 0.7266055 0.8419118 0.6934985 0.6481928
## L3 0.7272727 0.6652720 0.7903226 0.6466431 0.8684211 0.7300771 0.6474359
## L4 0.6855670 0.6699858 0.7736390 0.6373626 0.8485804 0.6514286 0.6050228
## L5 0.7180617 0.6110345 0.7864078 0.6600985 0.8708010 0.7109005 0.5975359
## L6 0.7363184 0.6987448 0.7652174 0.6890459 0.8512658 0.7052342 0.6252822
## L7 0.7182320 0.7354196 0.7434211 0.6904315 0.8272059 0.6645768 0.6286408
## L8 0.7425150 0.7753623 0.7797834 0.7251462 0.8396624 0.7006803 0.6909548
## L9 0.7683824 0.8639761 0.7894737 0.8316222 0.7417219 0.7307692 0.7718310
## A9 C1 C10 C11 C12 C2 C22
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1 0.7972973
## C10 0.7186701 0.6698565
## C11 0.8453039 0.7337662 0.7635468
## C12 0.6565421 0.7200000 0.6490066 0.7642586
## C2 0.7109005 0.6844262 0.6389892 0.7594937 0.6656805
## C22 0.7831633 0.7609756 0.6283186 0.8052632 0.7114094 0.6828358
## C23 0.7406417 0.6878307 0.6367713 0.7722222 0.7142857 0.6806084 0.6854460
## C24 0.7967033 0.6728395 0.6780488 0.7297297 0.7454545 0.7183673 0.6720430
## C25 0.7924528 0.6764706 0.6682464 0.7547170 0.7455830 0.6987952 0.7213930
## C26 0.7706667 0.6994536 0.6513761 0.7588235 0.7177700 0.6784314 0.6567164
## C27 0.7400881 0.7400722 0.7101911 0.8074074 0.7493473 0.6341463 0.7508197
## C28 0.7211740 0.7714286 0.7270115 0.8424437 0.7512077 0.6611570 0.7748538
## C29 0.7990544 0.7608696 0.7689531 0.8482143 0.7900875 0.6983051 0.7713178
## C3 0.7606383 0.6721311 0.5693780 0.7218935 0.6866197 0.6588235 0.6519608
## C30 0.7834101 0.7291667 0.7377622 0.8119658 0.7677054 0.6797386 0.7518519
## C31 0.7958237 0.7510549 0.7473310 0.8354978 0.7828571 0.6963696 0.7806691
## C32 0.7682927 0.7064220 0.6745098 0.7740385 0.7439024 0.6537102 0.7183673
## C34 0.7635575 0.7824561 0.7406250 0.8530466 0.7706186 0.6843658 0.7929936
## C35 0.7808219 0.7510040 0.7301038 0.8360656 0.7715877 0.6688312 0.7527273
## C36 0.7865429 0.7341772 0.7464789 0.8181818 0.7784091 0.6788079 0.7749077
## C37 0.7522321 0.7175573 0.7147541 0.8069498 0.7197802 0.6406250 0.7525424
## C38 0.7592593 0.7628458 0.7275862 0.8326531 0.7694444 0.6535948 0.7634409
## C39 0.8032407 0.7489362 0.7364621 0.8185841 0.7920228 0.6632653 0.7651515
## C4 0.7199074 0.6693548 0.6263345 0.7782258 0.6510264 0.6205788 0.7071429
## C40 0.7619048 0.7576923 0.7324415 0.8300395 0.7656676 0.6254072 0.7543860
## C5 0.9059829 0.7894737 0.8270270 0.7476636 0.8764479 0.8235294 0.8443114
## C6 0.8700565 0.7535211 0.7606383 0.7500000 0.8282443 0.7894737 0.8125000
## C7 0.8123324 0.6585366 0.6730769 0.6959459 0.7272727 0.7137097 0.7208122
## C8 0.7202073 0.6030928 0.6085106 0.6810811 0.6498316 0.6037736 0.6939655
## C9 0.7429306 0.6831683 0.6000000 0.7486911 0.6621622 0.6630037 0.6929825
## L1 0.7602041 0.7476190 0.7032520 0.7578947 0.7156863 0.7342657 0.7552743
## L10 0.6805252 0.7581699 0.7176471 0.8193980 0.6684073 0.7018970 0.7515152
## L11 0.6541850 0.7912773 0.7204611 0.8354839 0.6649485 0.6978610 0.7647059
## L12 0.7265823 0.7214612 0.6823529 0.8009479 0.6942675 0.7281879 0.7213115
## L14 0.6546610 0.7810651 0.7247956 0.8473054 0.6908213 0.7095960 0.7666667
## L15 0.6877637 0.8053892 0.7534247 0.8038585 0.7222222 0.7455919 0.8060942
## L18 0.6296296 0.7659574 0.6985915 0.8409786 0.6566416 0.6745407 0.7304348
## L19 0.6361656 0.7638037 0.6772334 0.8286604 0.6506329 0.6892950 0.7606838
## L2 0.6207585 0.7844156 0.6997519 0.8364116 0.6561798 0.6595745 0.7804878
## L20 0.6687243 0.7780980 0.6885246 0.8323529 0.6746411 0.6859296 0.7608696
## L21 0.6924779 0.7440273 0.6539683 0.8401361 0.6929134 0.6657143 0.7460815
## L22 0.6176471 0.7980050 0.7098321 0.8596491 0.6578947 0.6943820 0.7732697
## L23 0.6945701 0.7578947 0.6601307 0.8362989 0.6883469 0.7020057 0.7508091
## L24 0.6979405 0.7598566 0.6732673 0.8315018 0.6958904 0.6988304 0.7315436
## L3 0.6864754 0.8091168 0.7154472 0.8258258 0.6699029 0.6606218 0.7671233
## L4 0.6395604 0.7777778 0.7011494 0.8173077 0.6443299 0.6939314 0.7485380
## L5 0.6608527 0.7906977 0.7373494 0.8455497 0.6681514 0.6965517 0.7893462
## L6 0.6844350 0.7648903 0.7359551 0.8199357 0.6834171 0.6992084 0.7863248
## L7 0.6766744 0.7544484 0.7028754 0.7947761 0.6740331 0.6794118 0.7434211
## L8 0.7283654 0.7611336 0.7163121 0.8127660 0.7023810 0.6977492 0.7388060
## L9 0.8096515 0.6686747 0.7235023 0.7798742 0.7553191 0.7548638 0.7836538
## C23 C24 C25 C26 C27 C28 C29
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24 0.6611111
## C25 0.7258883 0.6543210
## C26 0.5957447 0.6250000 0.6902174
## C27 0.7068966 0.7785714 0.7649123 0.7370242
## C28 0.7292308 0.7859425 0.7888199 0.7484472 0.3937500
## C29 0.7357724 0.7860262 0.7899160 0.7613169 0.4452830 0.4641638
## C3 0.6748768 0.6279070 0.6702703 0.6296296 0.7285223 0.7289720 0.7407407
## C30 0.7070312 0.7478992 0.7479675 0.6951220 0.3698113 0.4488449 0.3788546
## C31 0.7470817 0.7702128 0.7795918 0.7213115 0.3916350 0.4633333 0.3901345
## C32 0.7112971 0.7327189 0.7333333 0.6929825 0.4448669 0.4898649 0.4318182
## C34 0.7525084 0.8153310 0.7762238 0.7736486 0.4252492 0.4246154 0.4351145
## C35 0.7330827 0.7642276 0.7588933 0.7176471 0.4357143 0.4133333 0.3739130
## C36 0.7315175 0.7637131 0.7632653 0.7509881 0.3947368 0.4703947 0.4217391
## C37 0.7117438 0.7769517 0.7490775 0.7292419 0.3523132 0.4294671 0.4166667
## C38 0.7303371 0.7952756 0.7509881 0.7442748 0.3941606 0.4066667 0.4000000
## C39 0.7351779 0.7787234 0.7727273 0.7398374 0.4053030 0.4493243 0.3698630
## C4 0.6911765 0.7283465 0.7386364 0.6743295 0.7053824 0.6973684 0.7322581
## C40 0.7216117 0.7984791 0.7835821 0.7490775 0.4042553 0.4358974 0.4180328
## C5 0.8238994 0.8153846 0.8201439 0.7959184 0.8750000 0.9096990 0.8965517
## C6 0.7546012 0.7022901 0.7412587 0.7385621 0.8352490 0.8445946 0.8177340
## C7 0.6483516 0.6932515 0.6959064 0.6514286 0.7242647 0.7620579 0.7695652
## C8 0.6313364 0.6733668 0.6763285 0.6651163 0.6821192 0.6867470 0.7720588
## C9 0.6666667 0.6717949 0.6813725 0.6243902 0.6856187 0.6614907 0.6944444
## L1 0.7379913 0.7326733 0.7570093 0.7488789 0.7539936 0.7665706 0.7881041
## L10 0.7469136 0.8006431 0.7993730 0.7663551 0.7512315 0.7471264 0.7863014
## L11 0.7567568 0.7981073 0.8006135 0.7757576 0.6862245 0.6988235 0.7744565
## L12 0.6923077 0.7122642 0.7252252 0.6844444 0.7476923 0.7393768 0.7706093
## L14 0.7696629 0.7910448 0.8005780 0.7806268 0.7347418 0.7206208 0.8005051
## L15 0.7681159 0.8012232 0.8176471 0.7971014 0.7738928 0.7712418 0.8333333
## L18 0.7657143 0.7723077 0.7606061 0.7250755 0.7094431 0.6993166 0.7754569
## L19 0.7345133 0.7739938 0.7771084 0.7303030 0.6945813 0.6912442 0.7671958
## L2 0.7493671 0.8056995 0.7829457 0.7746835 0.7121212 0.6839917 0.7708333
## L20 0.7671233 0.8017241 0.8005618 0.7535411 0.7420091 0.7306034 0.7945545
## L21 0.7371795 0.7845118 0.7674419 0.7326733 0.7404580 0.7279236 0.7859155
## L22 0.7759036 0.8154613 0.8054187 0.7823961 0.7232704 0.7034068 0.7942478
## L23 0.7458746 0.7651246 0.7689655 0.7372014 0.7538860 0.7341463 0.7877907
## L24 0.7641196 0.7717391 0.7667845 0.7386760 0.7585302 0.7413793 0.7899408
## L3 0.7597765 0.7947214 0.7971429 0.7600000 0.7206573 0.6928251 0.7634961
## L4 0.7212121 0.7805643 0.7723077 0.7477204 0.7336562 0.7073733 0.7857143
## L5 0.7650000 0.8149100 0.8136020 0.7839196 0.7473684 0.7263581 0.8013544
## L6 0.7500000 0.8018576 0.8006042 0.7500000 0.7445783 0.7324263 0.7845745
## L7 0.7382550 0.7706093 0.7785467 0.7379310 0.7480315 0.7346437 0.7817109
## L8 0.7328244 0.7593361 0.7640000 0.7272727 0.7663818 0.7605263 0.7781457
## L9 0.7650000 0.7337278 0.7272727 0.7393617 0.7703180 0.7784810 0.7543860
## C3 C30 C31 C32 C34 C35 C36
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24
## C25
## C26
## C27
## C28
## C29
## C3
## C30 0.7011952
## C31 0.7269076 0.3521739
## C32 0.6651982 0.4533898 0.4247788
## C34 0.7397260 0.4363636 0.4291045 0.4869888
## C35 0.7181467 0.3983740 0.3617021 0.4411765 0.4593640
## C36 0.7160000 0.3702128 0.3879310 0.4482759 0.4432234 0.4314516
## C37 0.7065217 0.4128788 0.3984375 0.4409449 0.4425676 0.4259259 0.3828125
## C38 0.7300380 0.3902439 0.3941909 0.4393305 0.4130435 0.3984064 0.4105691
## C39 0.7037037 0.3819742 0.3423423 0.4675325 0.4307116 0.3771186 0.3612335
## C4 0.6804511 0.7173913 0.7335423 0.7086093 0.7290503 0.7147239 0.7210031
## C40 0.7259259 0.4263566 0.3927126 0.4801587 0.4225352 0.4153846 0.4335938
## C5 0.8104575 0.8755760 0.8809524 0.8724490 0.9125475 0.8888889 0.9086758
## C6 0.7307692 0.8073394 0.8433180 0.8150000 0.8814815 0.8434783 0.8302752
## C7 0.6228571 0.7375000 0.7489362 0.7096774 0.7852113 0.7640000 0.7372881
## C8 0.6095238 0.6985294 0.7222222 0.6865079 0.7388535 0.7234043 0.6928839
## C9 0.5650000 0.6525097 0.6821705 0.6186441 0.7052980 0.6501901 0.6870229
## L1 0.7031963 0.7773852 0.7472119 0.7382812 0.7875000 0.7649123 0.7689531
## L10 0.7310127 0.7653333 0.7828418 0.7675070 0.7592138 0.7786458 0.7754011
## L11 0.7298137 0.7506631 0.7680000 0.7308782 0.7270471 0.7545692 0.7540107
## L12 0.6740088 0.7568493 0.7622378 0.7072243 0.7764350 0.7491525 0.7697595
## L14 0.7376093 0.7807882 0.7880299 0.7708333 0.7655172 0.7869249 0.7688442
## L15 0.7861272 0.8098765 0.8292079 0.8200514 0.8063781 0.8240964 0.8163772
## L18 0.7177177 0.7493606 0.7596899 0.7405405 0.7204819 0.7436548 0.7525773
## L19 0.7033639 0.7343750 0.7447368 0.7315068 0.7184466 0.7448980 0.7441253
## L2 0.7493606 0.7590090 0.7511521 0.7488152 0.7136659 0.7312073 0.7534247
## L20 0.7321937 0.7603912 0.7733990 0.7525773 0.7291667 0.7668269 0.7815534
## L21 0.7119205 0.7643836 0.7653631 0.7630058 0.7518987 0.7513661 0.7814208
## L22 0.7425000 0.7500000 0.7699115 0.7540230 0.7406639 0.7693966 0.7746171
## L23 0.6888112 0.7690141 0.7630058 0.7416413 0.7427822 0.7591036 0.7727273
## L24 0.7077465 0.7672414 0.7861272 0.7507692 0.7473404 0.7642045 0.7851003
## L3 0.7457143 0.7629630 0.7480916 0.7486911 0.7071429 0.7356608 0.7568922
## L4 0.7009346 0.7686375 0.7792208 0.7506849 0.7415459 0.7595908 0.7877238
## L5 0.7620253 0.7890110 0.7820225 0.7862069 0.7489451 0.7704194 0.7893570
## L6 0.7574850 0.7674419 0.7716535 0.7629428 0.7553957 0.7709924 0.7835052
## L7 0.6936620 0.7449275 0.7710145 0.7575758 0.7726098 0.7563739 0.7594203
## L8 0.7023810 0.7805643 0.7581699 0.7465753 0.7650430 0.7732919 0.7612903
## L9 0.6629834 0.7489712 0.7500000 0.7110092 0.7816901 0.7550201 0.7541667
## C37 C38 C39 C4 C40 C5 C6
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24
## C25
## C26
## C27
## C28
## C29
## C3
## C30
## C31
## C32
## C34
## C35
## C36
## C37
## C38 0.3576923
## C39 0.4000000 0.3039648
## C4 0.6863905 0.7280967 0.7398119
## C40 0.3992674 0.3032787 0.3333333 0.7095808
## C5 0.8911290 0.8995633 0.8851675 0.8454936 0.9083333
## C6 0.8266129 0.8448276 0.8142857 0.8125000 0.8416667 0.7391304
## C7 0.7396226 0.7560000 0.7413793 0.7086614 0.7748092 0.7280000 0.7226277
## C8 0.6815068 0.7298246 0.7296296 0.5858209 0.7172414 0.8268156 0.7914439
## C9 0.6805556 0.6923077 0.6533865 0.6784452 0.6750903 0.8352273 0.7514124
## L1 0.7475083 0.7793103 0.7728938 0.7162630 0.7661017 0.8715084 0.8152174
## L10 0.7525253 0.7766234 0.7783784 0.6986667 0.7634961 0.8961938 0.8605442
## L11 0.7128205 0.7328042 0.7500000 0.6981627 0.7198953 0.9063545 0.8679868
## L12 0.7207792 0.7466216 0.7605634 0.7278689 0.7629870 0.8652850 0.7886598
## L14 0.7201946 0.7580247 0.7930175 0.7000000 0.7578692 0.9133127 0.8707692
## L15 0.7843602 0.8106796 0.8283582 0.7481481 0.8095238 0.9061489 0.8726115
## L18 0.6934673 0.7353690 0.7552083 0.6854220 0.7131980 0.9116719 0.8647799
## L19 0.7042607 0.7397959 0.7400531 0.6930946 0.7240506 0.9073482 0.8262295
## L2 0.7008929 0.7295455 0.7528868 0.6697460 0.7270694 0.9029650 0.8750000
## L20 0.7159905 0.7649880 0.7783251 0.7024390 0.7529691 0.9066265 0.8618619
## L21 0.7146667 0.7527174 0.7743733 0.6994536 0.7426273 0.8916968 0.8586572
## L22 0.7099567 0.7434211 0.7770419 0.6946903 0.7326087 0.9121447 0.8708010
## L23 0.7107438 0.7569832 0.7723343 0.7022472 0.7465565 0.8943396 0.8339623
## L24 0.7397260 0.7655367 0.7884058 0.7138810 0.7753425 0.8910506 0.8196078
## L3 0.7087379 0.7369727 0.7305699 0.6817043 0.7121588 0.8947368 0.8523077
## L4 0.7218045 0.7480720 0.7780679 0.6736842 0.7449495 0.8907285 0.8603896
## L5 0.7180617 0.7605322 0.7919463 0.6819222 0.7576419 0.9088472 0.8750000
## L6 0.7300000 0.7627551 0.7671958 0.6961039 0.7593985 0.8862876 0.8631922
## L7 0.7252747 0.7647059 0.7803468 0.6914286 0.7575758 0.8798450 0.8314176
## L8 0.7349398 0.7822086 0.7647059 0.7182663 0.7629179 0.8761468 0.8348214
## L9 0.7500000 0.7716535 0.7531915 0.7859779 0.7803030 0.8489209 0.7862069
## C7 C8 C9 L1 L10 L11 L12
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24
## C25
## C26
## C27
## C28
## C29
## C3
## C30
## C31
## C32
## C34
## C35
## C36
## C37
## C38
## C39
## C4
## C40
## C5
## C6
## C7
## C8 0.6414141
## C9 0.6800000 0.6244541
## L1 0.7746479 0.7053942 0.6936170
## L10 0.7923323 0.6960486 0.7073171 0.6331169
## L11 0.8012422 0.6955224 0.6909091 0.6509434 0.5122616
## L12 0.7305936 0.6733871 0.6556017 0.6223176 0.5779221 0.6105919
## L14 0.8285714 0.7050562 0.7298050 0.6557864 0.5233161 0.4869110 0.6297376
## L15 0.8254438 0.7344633 0.7660167 0.6826347 0.5494792 0.5400517 0.6276276
## L18 0.7904192 0.6811594 0.7106017 0.6891496 0.5463918 0.5445293 0.6312684
## L19 0.7507788 0.6744868 0.6891496 0.7130435 0.5503876 0.5594937 0.6156156
## L2 0.7896104 0.6911392 0.7037975 0.6605263 0.5609195 0.5324074 0.6615776
## L20 0.8146067 0.7076503 0.7107438 0.7011173 0.5907990 0.6018957 0.6758242
## L21 0.7635135 0.6719745 0.7084639 0.7138365 0.6010638 0.6057441 0.6433121
## L22 0.8118812 0.6921182 0.7201946 0.7336562 0.6056645 0.5943601 0.6829268
## L23 0.7605634 0.6915584 0.6907895 0.6789298 0.5658263 0.5907859 0.6174497
## L24 0.7887324 0.7091503 0.7128713 0.6836735 0.5882353 0.6419098 0.6442953
## L3 0.7692308 0.7027778 0.6875000 0.6578947 0.5671642 0.5721271 0.6740947
## L4 0.7801858 0.6873156 0.6865672 0.6170886 0.5027027 0.4560440 0.5900621
## L5 0.8112245 0.7202970 0.7450980 0.6462766 0.5235849 0.5117371 0.6547315
## L6 0.7901235 0.7159420 0.7304348 0.6697248 0.5040650 0.5224274 0.6303030
## L7 0.7832168 0.6474576 0.6866667 0.6134752 0.5146199 0.5546218 0.5833333
## L8 0.7692308 0.6996337 0.6742424 0.6120000 0.5545171 0.6158358 0.6000000
## L9 0.7413793 0.7142857 0.6616162 0.6063830 0.6794425 0.6969697 0.6188119
## L14 L15 L18 L19 L2 L20 L21
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24
## C25
## C26
## C27
## C28
## C29
## C3
## C30
## C31
## C32
## C34
## C35
## C36
## C37
## C38
## C39
## C4
## C40
## C5
## C6
## C7
## C8
## C9
## L1
## L10
## L11
## L12
## L14
## L15 0.5088608
## L18 0.5463415 0.5985577
## L19 0.5847255 0.6024096 0.5176768
## L2 0.5180180 0.5792952 0.5826087 0.5860566
## L20 0.5917431 0.6278539 0.5024631 0.5421687 0.5653105
## L21 0.5581395 0.6442786 0.4918033 0.5514512 0.6294643 0.5194805
## L22 0.5911950 0.6299376 0.5265487 0.5331858 0.5808967 0.4900662 0.5462963
## L23 0.5796345 0.6135770 0.5138122 0.5138889 0.5915493 0.5173333 0.4984985
## L24 0.5857520 0.6520619 0.5399449 0.5718157 0.6545455 0.5189189 0.5400593
## L3 0.5558195 0.6353211 0.5967366 0.5938967 0.4977376 0.5972851 0.6507177
## L4 0.4247312 0.5116883 0.5353535 0.5753086 0.5069767 0.5761905 0.5699208
## L5 0.4733179 0.5273973 0.5701754 0.5860566 0.4678112 0.5560345 0.5687646
## L6 0.5289673 0.5091384 0.5902439 0.6009732 0.5553047 0.6009390 0.6189258
## L7 0.5365854 0.5601093 0.5760000 0.5840000 0.5356265 0.5989848 0.6145251
## L8 0.5828571 0.6160458 0.6312849 0.6242938 0.5954198 0.6631579 0.6666667
## L9 0.7034700 0.7324841 0.7343750 0.7197452 0.7412399 0.7410714 0.7301038
## L22 L23 L24 L3 L4 L5 L6
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24
## C25
## C26
## C27
## C28
## C29
## C3
## C30
## C31
## C32
## C34
## C35
## C36
## C37
## C38
## C39
## C4
## C40
## C5
## C6
## C7
## C8
## C9
## L1
## L10
## L11
## L12
## L14
## L15
## L18
## L19
## L2
## L20
## L21
## L22
## L23 0.5380952
## L24 0.5807963 0.5201238
## L3 0.5904366 0.6146096 0.6584158
## L4 0.5892473 0.5844504 0.5983827 0.5273632
## L5 0.5753425 0.5849057 0.6200466 0.5374449 0.4455206
## L6 0.6202532 0.6047745 0.6332454 0.5714286 0.5091864 0.5150812
## L7 0.6322870 0.5865103 0.6260870 0.5706806 0.4633431 0.5210918 0.5659341
## L8 0.6744731 0.6355140 0.6645963 0.5819209 0.5321101 0.5954198 0.6224784
## L9 0.7760814 0.6828358 0.7030075 0.7514970 0.7049180 0.7479893 0.7072368
## L7 L8
## A10
## A11
## A12
## A13
## A16
## A2
## A26
## A27
## A28
## A3
## A30
## A31
## A32
## A33
## A34
## A4
## A5
## A6
## A7
## A8
## A9
## C1
## C10
## C11
## C12
## C2
## C22
## C23
## C24
## C25
## C26
## C27
## C28
## C29
## C3
## C30
## C31
## C32
## C34
## C35
## C36
## C37
## C38
## C39
## C4
## C40
## C5
## C6
## C7
## C8
## C9
## L1
## L10
## L11
## L12
## L14
## L15
## L18
## L19
## L2
## L20
## L21
## L22
## L23
## L24
## L3
## L4
## L5
## L6
## L7
## L8 0.5387205
## L9 0.6367188 0.6444444Using qualitative distances
Now you can run the ordination function in Phyloseq and then plot it.
qual_ord <- ordinate(physeq.rarefied, method="PCoA", distance=jac_dist)plot_jac <- plot_ordination(physeq.rarefied, qual_ord, color="Name", title='Jaccard') + theme(aspect.ratio=1) + geom_point(size=4)
plot_jac
Using quantitative distances
For quantitative distance use the Bray-Curtis method:
bc_dist <- distance(physeq.rarefied, method = "bray", binary = FALSE)Now run ordination on the quantitative distances and plot them
quant_ord <- ordinate(physeq.rarefied, method="PCoA", distance=bc_dist)plot_bc <- plot_ordination(physeq.rarefied, quant_ord, color="Name", title='Bray-Curtis') + theme(aspect.ratio=1) + geom_point(size=3)
plot_bc
plot_Weevils_parasitization <- plot_ordination(physeq.rarefied, quant_ord, color = "Name", shape = 'Parasitization', title = "Jaccard Weevils and Parasitization") + theme(aspect.ratio = 1) + geom_point(size=1)
plot_Weevils_parasitization
plot_Weevils_location <- plot_ordination(physeq.rarefied, quant_ord, color = "Name", shape = 'Site', title = "Jaccard Weevils and Sites") + theme(aspect.ratio = 1) + geom_point(size=1)
plot_Weevils_location
# Permutational ANOVA
adonis(bc_dist ~ sample_data(physeq.rarefied)$Name)##
## Call:
## adonis(formula = bc_dist ~ sample_data(physeq.rarefied)$Name)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(physeq.rarefied)$Name 2 10.647 5.3235 26.124 0.42739 0.001
## Residuals 70 14.264 0.2038 0.57261
## Total 72 24.912 1.00000
##
## sample_data(physeq.rarefied)$Name ***
## Residuals
## Total
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1adonis(bc_dist ~ sample_data(physeq.rarefied)$Parasitization)##
## Call:
## adonis(formula = bc_dist ~ sample_data(physeq.rarefied)$Parasitization)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model
## sample_data(physeq.rarefied)$Parasitization 1 0.4248 0.42480 1.2317
## Residuals 71 24.4868 0.34488
## Total 72 24.9116
## R2 Pr(>F)
## sample_data(physeq.rarefied)$Parasitization 0.01705 0.275
## Residuals 0.98295
## Total 1.00000adonis(bc_dist ~ sample_data(physeq.rarefied)$Site)##
## Call:
## adonis(formula = bc_dist ~ sample_data(physeq.rarefied)$Site)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(physeq.rarefied)$Site 2 4.4036 2.20178 7.5153 0.17677 0.001
## Residuals 70 20.5080 0.29297 0.82323
## Total 72 24.9116 1.00000
##
## sample_data(physeq.rarefied)$Site ***
## Residuals
## Total
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1adonis(bc_dist ~ sample_data(physeq.rarefied)$Sex)##
## Call:
## adonis(formula = bc_dist ~ sample_data(physeq.rarefied)$Sex)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(physeq.rarefied)$Sex 2 0.6609 0.33044 0.95383 0.02653 0.507
## Residuals 70 24.2507 0.34644 0.97347
## Total 72 24.9116 1.00000Subsetting by taxonomy
Rickettisia_subset
Ricket <- subset_taxa(physeq.rarefied, Order=='Rickettsiales')
Ricket## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 4 taxa and 73 samples ]
## sample_data() Sample Data: [ 73 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 4 taxa by 7 taxonomic ranks ]plot_bar(Ricket, fill = 'Genus')
plot_bar(Ricket, fill = 'Family')
barplot5 <- plot_bar(Ricket, x="Parasitization", fill = 'Genus') + facet_wrap(~Name, scales = "free_x", nrow = 1)
barplot5
Subsampling for Buchnera genus
Enterobacte <- subset_taxa(physeq.rarefied, Family=='Enterobacteriaceae')
Enterobacte## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 23 taxa and 73 samples ]
## sample_data() Sample Data: [ 73 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 23 taxa by 7 taxonomic ranks ]plot_bar(Enterobacte, fill = 'Genus')
barplot6 <- plot_bar(Enterobacte, x="Parasitization", fill = 'Genus') + facet_wrap(~Name, scales = "free_x", nrow = 1)
barplot6
alphab <- subset_taxa(physeq.rarefied, Class=='Alphaproteobacteria')plot_bar(alphab, fill = 'Family')
Subsampling for CRW
CRW <- subset_samples(physeq, Name=='Crw')
CRW## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1142 taxa and 30 samples ]
## sample_data() Sample Data: [ 30 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 1142 taxa by 7 taxonomic ranks ]Crw <-plot_bar(CRW, fill = 'Phylum')
Crw
crw.rarefied <- rarefy_even_depth(CRW, rngseed=1, sample.size = 0.9*min(sample_sums(CRW)), replace = F)## `set.seed(1)` was used to initialize repeatable random subsampling.## Please record this for your records so others can reproduce.## Try `set.seed(1); .Random.seed` for the full vector## ...## 410OTUs were removed because they are no longer
## present in any sample after random subsampling## ...Now, calculate the distance and ordination
crw_dist <- distance(crw.rarefied, method = 'bray', binary=FALSE)
crw_ord <- ordinate(crw.rarefied, method="PCoA", distance=crw_dist)plot the ordination
plot_crw <- plot_ordination(crw.rarefied, crw_ord, color='Site',shape='Parasitization', title="CRW Parasitization and Site PCoA")+
theme(aspect.ratio=1)+ geom_point(size=4)
plot_crw
Anova CRW parasitization
adonis(crw_dist ~ sample_data(crw.rarefied)$Parasitization)##
## Call:
## adonis(formula = crw_dist ~ sample_data(crw.rarefied)$Parasitization)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2
## sample_data(crw.rarefied)$Parasitization 1 0.1253 0.12528 0.62392 0.0218
## Residuals 28 5.6225 0.20080 0.9782
## Total 29 5.7477 1.0000
## Pr(>F)
## sample_data(crw.rarefied)$Parasitization 0.626
## Residuals
## TotalNot significant
adonis(crw_dist ~ sample_data(crw.rarefied)$Site)##
## Call:
## adonis(formula = crw_dist ~ sample_data(crw.rarefied)$Site)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(crw.rarefied)$Site 1 0.8377 0.83771 4.7772 0.14575 0.011 *
## Residuals 28 4.9100 0.17536 0.85425
## Total 29 5.7477 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Significant
adonis(crw_dist ~ sample_data(crw.rarefied)$Sex)##
## Call:
## adonis(formula = crw_dist ~ sample_data(crw.rarefied)$Sex)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(crw.rarefied)$Sex 1 0.2057 0.20566 1.0391 0.03578 0.321
## Residuals 28 5.5421 0.19793 0.96422
## Total 29 5.7477 1.00000Not significant
Subsampling LW
LW <- subset_samples(physeq, Name=='Lw')
LW## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1142 taxa and 21 samples ]
## sample_data() Sample Data: [ 21 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 1142 taxa by 7 taxonomic ranks ]plot_bar(LW, fill = 'Phylum')
lw.rarefied <- rarefy_even_depth(LW, rngseed = 1, sample.size = 0.9*min(sample_sums(LW)), replace = F)## `set.seed(1)` was used to initialize repeatable random subsampling.## Please record this for your records so others can reproduce.## Try `set.seed(1); .Random.seed` for the full vector## ...## 333OTUs were removed because they are no longer
## present in any sample after random subsampling## ...lw_dist <- distance(lw.rarefied, method = 'bray', binary=FALSE)
lw_ord <- ordinate(lw.rarefied, method="PCoA", distance=lw_dist)plot_lw <- plot_ordination(lw.rarefied, lw_ord, color='Site',shape='Parasitization', title="LW Parasitization and Site PCoA")+
theme(aspect.ratio=1)+ geom_point(size=4)
plot_lw
Anova LW
adonis(lw_dist ~ sample_data(lw.rarefied)$Parasitization)##
## Call:
## adonis(formula = lw_dist ~ sample_data(lw.rarefied)$Parasitization)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2
## sample_data(lw.rarefied)$Parasitization 1 0.4119 0.41189 2.1194 0.10035
## Residuals 19 3.6926 0.19435 0.89965
## Total 20 4.1045 1.00000
## Pr(>F)
## sample_data(lw.rarefied)$Parasitization 0.115
## Residuals
## TotalNot significant
adonis(lw_dist ~ sample_data(lw.rarefied)$Site)##
## Call:
## adonis(formula = lw_dist ~ sample_data(lw.rarefied)$Site)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(lw.rarefied)$Site 1 1.1101 1.1101 7.0437 0.27046 0.002 **
## Residuals 19 2.9944 0.1576 0.72954
## Total 20 4.1045 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Significant, check how many samples you have per site, too low sample size will skew the result
adonis(lw_dist ~ sample_data(lw.rarefied)$Sex)##
## Call:
## adonis(formula = lw_dist ~ sample_data(lw.rarefied)$Sex)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(lw.rarefied)$Sex 1 0.2030 0.20298 0.98852 0.04945 0.383
## Residuals 19 3.9015 0.20534 0.95055
## Total 20 4.1045 1.00000Not significant
Subsampling for ASW
ASW <- subset_samples(physeq, Name=='Asw')
ASW## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1142 taxa and 22 samples ]
## sample_data() Sample Data: [ 22 samples by 4 sample variables ]
## tax_table() Taxonomy Table: [ 1142 taxa by 7 taxonomic ranks ]plot_bar(ASW, fill = 'Phylum')
Asw.rarefied <- rarefy_even_depth(ASW, rngseed = 1, sample.size = 0.9*min(sample_sums(ASW)), replace = F)## `set.seed(1)` was used to initialize repeatable random subsampling.## Please record this for your records so others can reproduce.## Try `set.seed(1); .Random.seed` for the full vector## ...## 109OTUs were removed because they are no longer
## present in any sample after random subsampling## ...plot_bar(Asw.rarefied, fill = 'Phylum')
asw_dist <- distance(Asw.rarefied, method = 'bray', binary=FALSE)
asw_ord <- ordinate(Asw.rarefied, method="PCoA", distance=asw_dist)plot_asw <- plot_ordination(Asw.rarefied, asw_ord, color='Parasitization',shape='Sex', title="ASW Parasitization and Sex PCoA")+
theme(aspect.ratio=1)+ geom_point(size=4)
plot_asw
adonis(asw_dist ~ sample_data(Asw.rarefied)$Parasitization)##
## Call:
## adonis(formula = asw_dist ~ sample_data(Asw.rarefied)$Parasitization)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2
## sample_data(Asw.rarefied)$Parasitization 1 0.8993 0.89926 5.117 0.20373
## Residuals 20 3.5148 0.17574 0.79627
## Total 21 4.4140 1.00000
## Pr(>F)
## sample_data(Asw.rarefied)$Parasitization 0.011 *
## Residuals
## Total
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Significant Check sample size
adonis(asw_dist ~ sample_data(Asw.rarefied)$Sex)##
## Call:
## adonis(formula = asw_dist ~ sample_data(Asw.rarefied)$Sex)
##
## Permutation: free
## Number of permutations: 999
##
## Terms added sequentially (first to last)
##
## Df SumsOfSqs MeanSqs F.Model R2 Pr(>F)
## sample_data(Asw.rarefied)$Sex 2 0.4816 0.24078 1.1634 0.1091 0.317
## Residuals 19 3.9325 0.20697 0.8909
## Total 21 4.4140 1.0000